We implement our models based on Faiss ANNS library [1]} and RecBole [2]}. We use \((M=32) \times (D=256)\)  PQ indices as the code representation scheme. We pre-train VQ-Rec for 300 epochs with temperature \(\tau =0.07\)  and semi-synthetic ratio \(\rho =0.75\) .
The iteration number for Gumbel-Sinkhorn algorithm is set to 3.
The main baseline results are taken from [3]} directly. We optimize the other models with an Adam optimizer [4]} and
search the hyper-parameters to find optimal results. The batch size is set to 2,048. The learning rate is tuned in \(\lbrace 0.0003, 0.001, 0.003\rbrace \) . The number of permutation learning epochs is tuned in \(\lbrace 3,5,10\rbrace \) . The models with the highest NDCG@10 results on validation set are selected for evaluation on test set. We adopt early stopping strategies with a patience of 10 epochs.
